Description of the desired feature

Read fatiando/contributing#19.
We can make use of actions/cache for caching the datasets available on Rockhound so we can reenable the full test suite on CIs, which was removed after #78.

Description of the desired feature

There are a bunch of Earth Models in the IRIS website besides PREM (see #10). We could have functions to download and load many of those, which would be interesting to plot and see their differences. See http://ds.iris.edu/ds/products/emc-referencemodels/

These functions wouldn't be too complicated and load the models into pandas.DataFrame, much like the fetch_prem function.

Intake is a general-purpose python package for accessing and cataloguing data. It is in use, amongst other places, by the Pangeo geo-atmospheric collaboration to reference several datasets (see a nice rendering of the nested catalogue structure at https://pangeo-data.github.io/pangeo-datastore/ ) and has integrations with scientific data collection descriptions such as STAC, THREDDS and CMIP.

It seems that we have some things in common! Particularly, Intake cares about careful description of datasets, a unified user API for inspecting and loading those data (into memory or dask, various containers), caching for load-on-first-access.

Lets start a conversation here about possible a integration, e.g., that Intake would show the rockhound collections as one of its catalogues.

cc @rabernat

I would like to request the Slab2 model to be added to Rockhound.
This model contains the three-dimensional geometries of all active global subduction zones.
This kind of data is important to create realistic simulations of subduction zones in geodynamics and correct gravity data.

• Model/dataset name: Slab2
• Download link: https://www.sciencebase.gov/catalog/item/5aa1b00ee4b0b1c392e86467
• License: CC0 https://github.com/usgs/slab2/blob/master/LICENSE.md
• Target audience: geophysicists, solid earth, etc
• Is it widely used: yes

Are you willing to help implement and maintain this feature? Yes

Hi everyone, I've been meaning to write this issue for a while but ended up forgetting. As you probably noticed, development of RockHound has pretty much stopped. The main reason is that the way we were going about adding datasets had several fatal flaws that made development unsustainable:

• No scope definition. We basically didn't define a scope for the project and so had no way of indicating which datasets would fit. This is mostly because we didn't know and thought we could just add everything here with no problem.
• CI and gallery infrastructure doesn't scale. As we started adding more datasets, our set up of downloading everything on CI to test and build the documentation started breaking. This is clearly not possible to do on free CI and would only get worse as the project grew.
• Lack of open licenses. Initially, the goal was to download and read into pandas/xarray all of the data and models that exits without clear licenses and in terrible custom formats (like CRUST1.0). This works fine for small files but as soon as the pre-processing gets more involved, this solution becomes difficult to maintain. The best way would be to repackage the datasets into a format that can be easily loaded with pandas/xarray but since these data have no license, we can't do that.

So development efforts slowly fizzled out because of these mounting problems that we had no good way of fixing. As such, we realized that development was not sustainable. A better approach to these problems is to build packages for specific datasets. That solves the CI/growth issue. So if anyone wants to pick that up, please feel free to use whatever code we have that helps you do that 🙂

For the future, we decided to shift our focus to curating a set of data that has open licenses and serves as good sample data for tutorials and documentation. Our packages have this already but we wanted to consolidate on a single package that can be shared. This effort has a much tighter scope so it's easier to know if we're going in the right direction.

We're gathering and packaging these datasets in https://github.com/fatiando-data and have created the Ensaio package to load them.

Sorry for the delay in this message and thank you to everyone who contributed to RockHound! We will be closing most PRs and Issues and putting a notice on the README linking to this issue.

Description of the desired feature

Include ETOPO1 from https://www.ngdc.noaa.gov/mgg/global/global.html

Load as xarray grids with option to choose the version between ice surface and bedrock. Quite a large download (~350Mb) so it might take a while the first time it's downloaded.

Description of the desired feature

Include CRUST1.0 model from https://igppweb.ucsd.edu/~gabi/crust1.html

Python code for loading the data from the archive file is in https://github.com/pinga-lab/paper-moho-inversion-tesseroids/blob/master/code/datasets.py#L205 though it uses a special class for the model, which we don't want. It would probably be better to have an xarray.Dataset since the model is a regular geographic grid.

Description of the desired feature

Wouldn't be better to change the Zenodo url to the Fatiando a Terra community?
It would make clearer that the release should be done through the New upload button on the community.

Are you willing to help implement and maintain this feature? Yes

We're almost ready for an initial 0.1.0 release. The only thing left is to use the new pooch.Decompress processor instead of the custom one implemented here. This will require bumping the pooch requirement to >=0.5.0. After that, we need:

• A changelog page marking the first release. Copy the format from https://github.com/fatiando/pooch/blob/master/doc/changes.rst Should only mark the first release with a note about which datasets it contains.
• Follow the steps in https://github.com/fatiando/rockhound/blob/master/MAINTENANCE.md including the Zenodo registration.
• Create a new package on conda-forge (see their docs). Should be straight forward since this can be a noarch: python package. See the recipe for Verde.
• Update the fatiando homepage to point to the latest version of the docs instead of dev and to say that the package can be used already even though it's changing.
• Tweet/LinkedIn/ResearchGate post about it 🎉

Description of the desired feature

Maybe we shouldn't be versioning the data cache location. This triggers new downloads of all datasets every time we get a new release. Since these datasets aren't likely to change without releasing a new version, we probably don't need this.

Description of the desired feature
Suggest adding the Mannville Group dataset of 2000+ well logs, tops, and facies labels from Alberta Geological Survey. It also has metadata and a report.
This dataset has been used in multiple hackathons. https://ags.aer.ca/publications/SPE_006.html

Different people with different goals might want to load different parts of that dataset. Here are some examples:

(1) Be able to load all data (excluding metadata and report) into a pandas dataframe for whatever purposes people might have
(2) Be able to load data that serves as a starter dataset for predicting tops. Different people might have different required curves or different tops, but there should be a starter dataset for that class of problem. For example, Top McMurray as the top and required curves are ['ILD', 'NPHI', 'GR', 'DPHI', 'DEPT'].
3) Be able to load all wells with facies + specific well log curves into a pandas dataframe for facies prediction. Required well log curves might be ['ILD', 'NPHI', 'GR', 'DPHI', 'DEPT'].

Are you willing to help implement and maintain this feature?

Yes.
Some useful starting points:

• Data is available from original source here: https://ags.aer.ca/publications/SPE_006.html
• Data is available in the Predictatops repo under the demo folder with wells that don't load right already taken out and put into a separate folder: https://github.com/JustinGOSSES/predictatops/tree/master/demo/mannville_demo_data
• An example of functions for interrogating the data and returning UWIs for wells that only have specific tops or curves can be found in the checkdata.py module of Predictatops.
• An example of functions for loading only the UWIs in a given well list can be found in the load.py module of Predictatops.

I'd like to make the data loading for Predictatops integrated with Pooch & Rockhound.

Description of the desired feature

CIs now download all files in registry while running tests and building the gallery.

After new datasets will be included into Rockhound, the amount of data needed to be fetched may grow too large that CIs may be not be capable of finishing the testing builds.
Because there's no way to permanently store this datasets on the CIs, we decided to remove test and gallery builds from being run on by the CIs. Instead, the maintainers should run the tests and build the gallery locally before each PR is merged into master. Test functions and gallery examples must still be included on every new PR (and 100% coverage is still a goal to achieve), but the difference is that CIs won't run them.
All files inside doc/gallery should be pushed to the repository so CIs can still build the docs (and deploy them) but avoiding to rerun the gallery examples.
A new minimal test function should be added that only checks if all files in the registry are available for download.
Add an extra point to PR checklist to remind contributors to build the gallery example and push any change on doc/gallery, and maintainers to run all tests before merging the PR.

Are you willing to help implement and maintain this feature? Yes

Description of the desired feature

The combination of latest sphinx (2.2.0), numpydoc, and sphinx_rtd_theme
causes a mangled rendering of the parameter list. We were pinning sphinx
to 1.8.5, which is getting old and causing dependency problems.
See discussion in fatiando/pooch#113.

A possible solution is to replace numpydoc with sphinx.ext.napoleon.
It solves the problem although it generates a slightly different output.
The class autosummary template must be modified in order to account for
missing methods summary block.

This replacement has been achieved by @leouieda on fatiando/pooch#122.
See the changes applied on that PR for inspiration.

When running the test suite the test for bedmap2 fails with the current version of rasterio. The issue appears to be in line 102 of fetch_bedmap2.py. A call to xr.open_rasterio requires an import from rasterio._base import gdal_version which fails. This problem can be resolved by using version 1.0.24 of rasterio.

Full code that generated the error

def test_bedmap2_multiple_datasets():
    "Load multiple datasets"
    # Test with two datasets with same shape
    grid = fetch_bedmap2(["bed", "surface"])
    assert set(grid.data_vars) == set(["bed", "surface"])
    # Test with "bed" and "lakemask_vostok"
    grid = fetch_bedmap2(["bed", "lakemask_vostok"])
    assert set(grid.data_vars) == set(["bed", "lakemask_vostok"])
    # Test with "bed" and "thickness_uncertainty_5km"
    grid = fetch_bedmap2(["bed", "thickness_uncertainty_5km"])
    assert set(grid.data_vars) == set(["bed", "thickness_uncertainty_5km"])

Full error message

________________________ test_bedmap2_multiple_datasets ________________________

    def test_bedmap2_multiple_datasets():
        "Load multiple datasets"
        # Test with two datasets with same shape
>       grid = fetch_bedmap2(["bed", "surface"])

../rockhound/tests/test_bedmap2.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../rockhound/bedmap2.py:102: in fetch_bedmap2
    array = xr.open_rasterio(get_fname(dataset, fnames), chunks=chunks, **kwargs)
/home/chet/anaconda3/envs/rockhound/lib/python3.7/site-packages/xarray/backends/rasterio_.py:214: in open_rasterio
    import rasterio
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    """Rasterio"""
    
    from __future__ import absolute_import
    
    from collections import namedtuple
    from contextlib import contextmanager
    import logging
    
    try:
        from pathlib import Path
    except ImportError:  # pragma: no cover
        class Path:
            pass
    
    try:
        from logging import NullHandler
    except ImportError:  # pragma: no cover
        class NullHandler(logging.Handler):
            def emit(self, record):
                pass
    
>   from rasterio._base import gdal_version
E   ImportError: libtiledb.so.1.6.0: cannot open shared object file: No such file or directory

/home/chet/anaconda3/envs/rockhound/lib/python3.7/site-packages/rasterio/__init__.py:22: ImportError

System information

• Operating system: Ubuntu 18.04.3 LTS
• Python installation (Anaconda, system, ETS): Anaconda
• Version of Python: 3.7.3
• Version of this package: 0.10 (Failure on fetch_models WIP pull request)
• If using conda, paste the output of conda list below:

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Description of the desired feature

Generating the bedmap2 example when building the docs can consume a lot of memory an may crash not so powerful systems.
Would be nice to prevent this from happening, or at least raise a warning before start building the docs in order to avoid annoying situations.
On #45 we have reduced the memory consumption when reading the Bedmap2 datasets by converting them into Dask arrays, but generating the plot is still loading the entire file into RAM.

One solution proposed by @leouieda on #45 would be to plot just a portion of the continent.
Another one would be to reduce the resolution, although we must filter it to prevent aliasing.
Any new idea is very welcome!

Are you willing to help implement and maintain this feature? Yes

Conda-forge is no longer building 3.5 packages since October 2018. We should probably remove it from our CIs and officially drop support for new releases. This will mean updating:

• doc/install.rst
• setup.py
• .travis.yml

Description of the desired feature

The classic seafloor age grid by Müller et al: https://www.ngdc.noaa.gov/mgg/ocean_age/ocean_age_2008.html

There are many flavors (age, uncertainty, etc) but they're all in netCDF files. Loading them with xarray should be straight forward. Depending on their side, they can be downloaded individually (like in ETOPO1) if they're big or all at once if they are small (and loaded into a single Dataset).

This is a list of models that would be interesting to include here:

• PREM
• CRUST1.0
• CRUST2.0
• GRAIL crustal thickness (https://zenodo.org/record/997347)
• Plate boundary contours (http://peterbird.name/publications/2003_PB2002/2003_PB2002.htm)
• Seafloor age (https://www.ngdc.noaa.gov/mgg/ocean_age/ocean_age_2008.html)
• ETOPO1 (https://www.ngdc.noaa.gov/mgg/global/global.html)
• Ocean sediment thickness (https://www.ngdc.noaa.gov/mgg/sedthick/index.html)

Desired dataset/model:

• Model/dataset name: SEG/EAGE Salt Dome Model
• Download links:
  • Direct Download: https://s3.amazonaws.com/open.source.geoscience/open_data/seg_eage_models_cd/Salt_Model_3D.tar.gz
  • Webpage: https://wiki.seg.org/wiki/SEG/EAGE_Salt_and_Overthrust_Models
• License: Attribution 4.0 International (CC BY 4.0)
• Target audience: geophysicists
• Is it widely used: Oh yeah.

Are you willing to help implement and maintain this feature? Somewhat... @prisae, would you want to help me and we could both do this?

NOTE: The SEG website is having some issues and has been down for a few days, but the direct download link still works.

Also, here is a notebook we put together to show how to download and visualize the model and the various accompanying surface meshes using PyVista: https://nbviewer.jupyter.org/github/pyvista/show-room/blob/master/visualize-data.ipynb

(that example notebook might move around so let me know if the link breaks in the future)

I would like to request the LITHO1.0 model to be added to Rockhound.
This model is a 1° tessellated model of the crust and uppermost mantle of the earth, extending into the upper mantle to include the lithospheric lid and underlying asthenosphere. The model is parameterized laterally by tessellated nodes and vertically as a series of geophysically identified layers, such as water, ice, sediments, crystalline crust, lithospheric lid, and asthenosphere.

Model/dataset name: LITHO1 (https://igppweb.ucsd.edu/~gabi/litho1.0.html)
Download link: igppweb.ucsd.edu/~gabi/litho1/litho1.0.tar.gz
License: I dont know it
Target audience: geophysicists, solid earth, etc
Is it widely used: yes

Are you willing to help implement and maintain this feature? Yes

Description of the desired feature

The Bedmap2 datasets are originally given in different data types. For example, the surface or bed datasets are int64, while the geoid is a float64.
Most of them include no data values, flagged as a great number.
When loading them, fetch_bedmap2() converts these no data values into nans.
This converts the data type of the dataset into float64 independently of its original data type.

Would be nice to at least have an optional argument in order to prevent the no data vals to be converted. This lets the user to get the datasets on the original data type.

Are you willing to help implement and maintain this feature? Yes

Description of the desired feature

When reading the Bedmap2 dataset a lot of memory is consumed because the entire file is loaded into memory. This may cause system crashes and/or very annoying situations.

One way to overcome this situation is using Dask to read larger-than-memory data dividing it into chunks.

@leouieda I think you had more solutions in mind. Would you like to discuss them?

Are you willing to help implement and maintain this feature? Yes

A lot of time and memory are wasted reading in whole large grids, like ETOPO1, even when only a small fraction is desired. The problem is that we store the compressed archives and need to unzip them in temporary folder every time. Since the files are temporary, we can't use xarray's lazy loading on the grids and must read them all into memory. This is not ideal.

This is a limitation of Pooch and need to think of a way of dealing with this. Ideas are welcome.

cc @santisoler

Description of the desired feature

With more 3D datasets to come like #50, it might be useful to have utilities in here that would make discretize, PyVista, PyGIMLi or GemPy meshes/types of these datasets.

While xarray is awesome, most users will want a type that they are familiar with and having a clear way to use these datasets with common libraries like those mentioned would help gain users (and developers).

Are you willing to help implement and maintain this feature? Yes/No

Definitely! But I'll need guidance on what these methods should be called and where/how they should be implemented.

Perhaps some sort of to_pyvista(), to_discretize(), etc. methods that will only execute if the needed library is available - thus keeping `rockhounds dependencies to a minimum?

Description of the desired feature

Would be nice if the gallery example corresponding to each dataset would explicit the license under it's released and a reference to its article (or doi). By facilitating this information we could encourage people to cite the models they use, and get to know under which license are they released.

Are you willing to help implement and maintain this feature? Yes

Description of the desired feature

Include PREM (http://ds.iris.edu/ds/products/emc-prem/). This should be relatively straight forward as the model can be downloaded in CSV format and quickly loaded with pandas.

Description of the desired feature

After fatiando/verde#177 raised by @prisae, fatiando/community#9 and fatiando/community#10 it has been decided that all docstrings must be wrapped to 79 characters per line.
We should warp all the existing docstrings in Rockhound to 79 characters and also configure flake8 to check for this when running the style checks.

Sadly, there's no way (yet) to automatically change all docstrings, but at least we can use flake8 to raise lines that fail. This can be done by setting max-doc-length to 79 characters on the flake8 configuration under setup.cfg.

In the last community call we decided to roll out release drafter on RockHound to test the automated changelog generation (see fatiando/contributing#15). I setup the following labels for this repository:

I would propose we configure all repos to have the same labels (with same colors) to make things easier for us. I'll open a PR to enable the Github Actions workflow for this so we can discuss other configuration options.

Are these labels OK?